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Intradialytic hypotension is a common problem during hemodialysis treatment. Despite several clinical variables have been authenticated for associations during dialysis session, the interaction effects between variables has not yet been presented. Our study aimed to investigate clinical factors associated with intradialytic hypotension by deep learning. A total of 279 participants with 780 hemodialysis sessions on an outpatient in a hospital-facilitated hemodialysis center were enrolled in March 2018. Associations between clinical factors and intradialytic hypotension were determined using linear regression method and deep neural network. A full-adjusted model indicated that intradialytic hypotension is positively associated with body mass index (Beta = 0.17, p = 0.028), hypertension comorbidity (Beta = 0.17, p = 0.008), and ultrafiltration amount (Beta = 0.31, p < 0.001), and is inversely associated with the ultrafiltration rate in a hemodialysis session (Beta = −0.30, p = 0.001). The 4-factor locus obtained by the deep neural network reached the maximum performance metrics evaluation (accuracy = 64.97± 0.94; true positive rate = 87.97 ± 2.73; positive predictive value = 66.74 ± 0.98; Matthews correlation coefficient = 0.19 ± 0.03). The prediction model obtained by the deep learning scheme could be a potential tool for the management of intradialytic hypotension. INDEX TERMS Hemodialysis, deep learning, intradialytic hypotension.

BackgroundThe function of a protein is determined by its native protein structure. Among many protein prediction methods, the Hydrophobic-Polar (HP) model, an ab initio method, simplifies the protein folding prediction process in order to reduce the prediction complexity.ResultsIn this study, the ions motion optimization (IMO) algorithm was combined with the greedy algorithm (namely IMOG) and implemented to the HP model for the protein folding prediction based on the 2D-triangular-lattice model. Prediction results showed that the integration method IMOG provided a better prediction efficiency in a HP model. Compared to others, our proposed method turned out as superior in its prediction ability and resilience for most of the test sequences. The efficiency of the proposed method was verified by the prediction results. The global search capability and the ability to escape from the local best solution of IMO combined with a local search (greedy algorithm) to the new algorithm IMOG greatly improve the search for the best solution with reliable protein folding prediction.ConclusionOverall, the HP model integrated with IMO and a greedy algorithm as IMOG provides an improved way of protein structure prediction of high stability, high efficiency, and outstanding performance.

Abstract. This work presents a novel GA-Taguchi-based feature selection method. Genetic algorithms are utilized with randomness for "global search" of the entire search space of the intractable search problem. Various genetic operations, including crossover, mutation, selection and replacement are performed to assist the search procedure in escaping from sub-optimal solutions. In each iteration in the proposed nature-inspired method, the Taguchi methods are employed for "local search" of the entire search space and thus can help explore better feature subsets for next iteration. The two-level orthogonal array is utilized for a well-organized and balanced comparison of two levels for features-a feature is or is not selected for pattern classification-and interactions among features. The signal-to-noise ratio (SNR) is then used to determine the robustness of the features. As a result, feature subset evaluation efforts can be significantly reduced and a superior feature subset with high classification performance can be obtained. Experiments are performed on different application domains to demonstrate the performance of the proposed nature-inspired method. The proposed hybrid GA-Taguchi-based approach, with wrapper nature, yields superior performance and improves classification accuracy in pattern classification.